In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural network that has successfully been applied to analyzing visual imagery.
A CNN-based object detector may (i) instruct one or more convolutional layers to apply convolution operations to an input image, to thereby generate a feature map corresponding to the input image, (ii) instruct an RPN (Region Proposal Network) to generate proposals corresponding to an object in the input image by using the feature map, (iii) instruct a pooling layer to apply at least one pooling operation to areas on the feature map corresponding to the proposals, to thereby generate one or more pooled feature maps, and (iv) instruct an FC (Fully Connected) layer to apply at least one fully connected operation to the acquired pooled feature maps to output class information and regression information for the object, to thereby detect the object on the input image.
Recently, a conventional surveillance system using such an object detector has been developed. The conventional surveillance system uses a conventional pedestrian detector specialized for detecting an object, i.e., a pedestrian, from an image inputted from a surveillance camera, and detects the pedestrian by referring to a hair style, texture pattern and shape of clothes.
However, in the conventional pedestrian detector, there is a problem that the pedestrian cannot be detected accurately in case that the pedestrian is in a unique style and/or pattern which is not found in training data, or in case that the pedestrian is wearing something similar to the surroundings, like wearing a black dress on a dark road. If the pedestrian attempts to exploit such vulnerabilities and tries to hide or conceal his/her presence, the surveillance system will have serious problems.
Therefore, in case the conventional pedestrian detector fails to detect the pedestrian, a monitoring personnel must add training data of the failed case to enhance the coverage of the training data, and periodically retrain the pedestrian detector.
However, periodic retraining of the pedestrian detector does not prevent the failed cases from happening, and there is a disadvantage that the retraining must be performed in order to compensate for every failed case, and that a separate manpower is required for monitoring whether there is any undetected objects.
Furthermore, it is also difficult to secure adeaquate training data to perform the retraining for the failed cases.